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Most teams scale Kubernetes by thinking about pods and nodes. At Render, Brian Stack ran into a different dimension: hundreds of thousands of namespaces per cluster, multiplied across DaemonSets that list-watch every namespace.
Brian explains how Render traced the issue through Calico and Vector, worked with upstream maintainers, and turned memory profiling into operational wins: lower node costs, lighter API-server load, and faster rollouts.
In this interview:
Why namespaces can become a hidden scaling bottleneck

How DaemonSets multiply memory and control-plane pressure

How profiling, staging clusters, and upstream collaboration freed 7 TiB

Why pushing from an 80% fix to a complete fix can make teams faster

Sponsor
This episode is sponsored by LearnKube — get started on your Kubernetes journey through comprehensive online, in-person or remote training.
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What happens when an AI agent stops generating Kubernetes YAML and starts operating the cluster directly?
Mike Solomon, software engineer at AIATELLA, explains how his team moved from a sprawling Helm setup to Markdown-driven infrastructure specs that Claude Code can execute, test, and refine.
You will learn
Why Helm became hard to maintain for a fast-moving medical infrastructure repo

How Claude debugged Argo, TLS conflicts, kubectl patches, and private registry credentials

How runbooks plus agent memory files capture failures so deployments become reproducible.

It is a practical look at where Kubernetes automation may be heading: less hand-written YAML, more precise intent, and a sharper definition of when the human must stay in the loop.
Sponsor
This episode is sponsored by LearnKube — get started on your Kubernetes journey through comprehensive online, in-person or remote training.
More info
Find all the links and info for this episode here: https://ku.bz/y70mLvWNs

Interested in sponsoring an episode? Learn more.

A single Kubernetes CRD for every service request turns small changes into full-platform reconciliations.
Alexander Held, former platform engineer at Mercedes-Benz Tech Innovation, describes a production refactor from a 2,000-line CRD to purpose-built resources and controllers. He shows how teams can model business workflows as Kubernetes APIs and then use owner references, finalizers, and events to keep platform operations predictable.
You will learn:
Why monolithic CRDs create performance and troubleshooting problems

How controllers turn database provisioning and backups into reconciliation loops

How finalizers clean up external resources such as S3 backups

Why Kubernetes events make platform workflows easier to debug

Sponsor
This episode is sponsored by LearnKube — get started on your Kubernetes journey through comprehensive online, in-person or remote training.
More info
Find all the links and info for this episode here: https://ku.bz/TGy4Qn7Qs

Interested in sponsoring an episode? Learn more.

Kelsey Hightower, Eric Abercrombie, and Julius Payne II reflect on life after achievement, entering the Kubernetes world for the first time, and how music, creativity, and lived experience shape the way they think about technology.
In this interview:
Why fundamentals, patience, and repetition still matter more than shortcuts

How Kubernetes, community, and confidence intersect for people entering cloud-native work

What hip-hop, production, and storytelling can teach us about ownership, authenticity, and finding your voice

Sponsor
This episode is sponsored by LearnKube — get started on your Kubernetes journey through comprehensive online, in-person or remote training.
More info
Find all the links and info for this episode here: https://ku.bz/czrCCXSLt

Interested in sponsoring an episode? Learn more.

You're running gRPC services in Kubernetes, load balancing looks fine on the dashboard — but some pods are burning at 80% CPU while others sit idle, and adding more replicas only partially helps.
Rohit Agrawal, a Staff Software Engineer on the traffic platform team at Databricks, explains why this happens and how his team replaced Kubernetes's default networking with a proxy-less, client-side load-balancing system built on the xDS protocol.
In this episode:
Why KubeProxy's Layer 4 routing breaks down under high-throughput gRPC: it picks a backend once per TCP connection, not per request

How Databricks built an Endpoint Discovery Service (EDS) that watches Kubernetes directly and streams real-time pod metadata to every client

How zone-aware spillover cut cross-availability-zone costs without sacrificing availability

Why CPU-based routing failed (monitoring lag creates oscillation) and what signals to use instead

The system has been running in production for three years across hundreds of services, handling millions of requests.
Sponsor
This episode is sponsored by LearnKube — get started on your Kubernetes journey through comprehensive online, in-person or remote training.
More info
Find all the links and info for this episode here: https://ku.bz/y803JMhBk

Interested in sponsoring an episode? Learn more.